
lr = 5e-6  # learning rate
accum_grid = 4
batch_size = 1
epochs = 10
max_length = 4096
n_model = 21128
# n_model = 768
# model_name_or_path = 'E:\\czy\\bert_attention\\bigbird'
model_name_or_path = 'E:\\czy\\bert_attention\\longformer'
# train_path = 'dataset/train_dataset.csv'
# valid_path = 'dataset/valid_dataset.csv'
# test_path = 'dataset/test0908.xlsx'
train_path = 'public_dataset/train_dataset.txt'
valid_path = 'public_dataset/valid_dataset.txt'
cls = {'C3-Art': 0, 'C4-Literature': 1, 'C5-Education': 2, 'C6-Philosophy': 3, 'C7-History': 4, 'C11-Space': 5,
       'C15-Energy': 6, 'C16-Electronics': 7, 'C17-Communication': 8, 'C19-Computer': 9, 'C23-Mine': 10,
       'C29-Transport': 11, 'C31-Enviornment': 12, 'C32-Agriculture': 13, 'C34-Economy': 14, 'C35-Law': 15,
       'C36-Medical': 16, 'C37-Military': 17, 'C38-Politics': 18, 'C39-Sports': 19}

# cls = {'6-内部通话': 0, '1-红色': 1, '2-橙色': 2, '3-黄色': 3, '4-蓝色': 4, '5-绿色': 5}